Application of machine learning techniques for warfarin dosage prediction: a case study on the MIMIC-III dataset by Aasim Ayaz Wani, Fatima Abeer

“…Our method could integrate into clinical workflows to enhance anticoagulation therapy in cases of missing data, with potential applications in other complex medical treatments.…”
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Improving the Generalizability and Robustness of Large-Scale Traffic Signal Control by Tianyu Shi, Francois-Xavier Devailly, Denis Larocque, Laurent Charlin

“…First, sensor failures and GPS occlusions create missing-data challenges and we show that recent methods remain brittle in the face of these missing data. …”
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Imputation based wind speed forecasting technique during abrupt changes in short term scenario by Karan Sareen, Bijaya Ketan Panigrahi, Tushar Shikhola, Ravi Nath Tripathi, Ashok Kumar Rajput

“…Further, it is required to handle the variety of scenarios e.g. cyber‐attacks, unexpected power device malfunction, communication/sensor outages etc. that can cause the missing data.This paper proposes and employs a de‐noising autoencoder algorithm for wind speed forecasting to ensure the handling of missing data information. …”
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Prediction of Sonic Log Values Using a Gradient Boosting Algorithm in the 'AB' Field by Rasif Nahari, Utama Widya, Ardhya Garini Sherly, Fitri Indriani Rista, Pratama Novian Putra Dhea

“…Sonic log data are particularly prone to such gaps, as they are newer and less common in older wells. To address missing data, machine learning algorithms, like gradient boosting, provide an effective solution. …”
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Improving the selection of object-analogues of oil and gas fields in designing reservoir engineering by Alexander V. Karsakov, Pavel N. Zyatikov, Kirill V. Sinebriukhov, Irina V. Sharf

“…The commissioning of a large number of new fields with limited amount of initial geological and physical data. To fill in the missing data, the selection of object-analogs is carried out. …”
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Preprocessing Approach for Power Transformer Maintenance Data Mining Based on k-Nearest Neighbor Completion and Principal Component Analysis by Moïse Manyol, Samuel Eke, Alphonse J. M. Massoma, Alain Biboum, Ruben Mouangue

“…In reality, many databases are characterized by attributes with outliers, redundant, and even more missing values. Missing data and outliers are ubiquitous in our databases, and imputation techniques will help us mitigate their influence. …”
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BAYESIAN ESTIMATION OF GENERALIZED EXPONENTIAL DISTRIBUTION WITH MULTIPLE CHANGE POINTS FOR TRUNCATED AND CENSORED DATA by HE ChaoBing

“…The complete-data likelihood function of generalized exponential distribution for truncated and censored data is obtained by filling in the missing data of the life variable using rejection method. …”
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Determining the risk factors of malaria and anemia in children between 6 and 59 months using the joint generalized linear mixed model on the 2021 Nigeria Malaria Indicator Survey d... by Talani Mhelembe, Shaun Ramroop, Faustin Habyarimana

“…The study’s novelty lies in its handling of missing data through imputation techniques, enhancing the reliability of findings.…”
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Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations. by Cécile Triay, Alice Boizet, Christopher Fragoso, Anestis Gkanogiannis, Jean-François Rami, Mathias Lorieux

“…The main issues with these low-coverage genotyping methods are (1) poor performance at heterozygous loci, (2) high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself. …”
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THE METHOD OF FORMING CAE MODELS ON THE EXAMPLE OF DESIGN AND TECHNOLOGICAL ELABORATION OF THE PLUNGER OF FORCED HYDROMACHINE by A. V. Puzanov

“…The paper proposed a method of forming CAE models from CAD models, taking into account simplification models (with the exception of nonfunctional elements, the use of symmetry, etc.), add the missing data (including the use of the properties of materials differ from the CAD model), the possibility of a multidisciplinary analysis in one or more software systems. …”
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Estimating Potential Evapotranspiration by Missing Temperature Data Reconstruction by Eladio Delgadillo-Ruiz, Eusebio Jr. Ventura-Ramos, Julián González Trinidad, Hugo Enrique Júnez-Ferreira, Carlos Francisco Bautista-Capetillo, Olivia Delgadillo-Ruiz

“…The purpose of this study was as follows: first, to apply a missing data reconstruction scheme in weather stations of the Rio Queretaro basin; second, to reduce the generated uncertainty of temperature data: mean, minimum, and maximum values in the evapotranspiration calculation which has a paramount importance in the manner of obtaining the water balance at any hydrological basin. …”
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POMA-C: A Framework for Solving the Problem of Precise Anesthesia Control Under Incomplete Observation Environment in Low-Income Areas by Yide Yu, Huijie Li, Dennis Wong, Anmin Hu, Jian Huo, Yan Ma, Yue Liu

“…Through comprehensive ablation experiments—where key observation dimensions are systematically reduced to simulate missing data—POMA-C demonstrates significantly higher decision accuracy and cumulative reward optimization compared to methods like Q-learning and human expertise, even in data-constrained environments. …”
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Completion of registration of risk factor variables during telephone vs on-site follow-up after myocardial infarction: a nationwide observational study in 101 199 patients from con... by Maria Bäck, Margret Leosdottir, Emil Hagström, Nermin Hadziosmanovic, Halldora Ögmundsdottir Michelsen, Mattias Ekström

“…At the 2-month follow-up, the proportion of missing data registered at on-site visits compared with telephone consultations was systolic blood pressure 2.4% (n=1729) vs 28.0% (n=5462), low-density lipoprotein cholesterol 9.1% (n=6525) vs 32.6% (n=6360), weight 20.1% (n=14 343) vs 43.0% (n=8401) and haemoglobin A1c for patients with diabetes mellitus 39.4% (n=4594) vs 69.4% (n=2225), p for all <0.0001. …”
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Comparison of the CHU-9D and the EQ-5D-Y instruments in children and young people with cerebral palsy: a cross-sectional study by Jennifer M Ryan, Nana Anokye, Grace Lavelle, Ellen McKay, Marika Noorkoiv, Nicola Theis

“…Associations between utility values and GMFCS level were examined to assess known-group differences.Results Missing data were <5% for both instruments. Twenty participants (32.3%) and 11 participants (18.0%) reported full health for the EQ-5D-Y and CHU-9D, respectively. …”
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Optimization of multiple sampling for solving network boundary specification problem by Ruochen Zhang

“…Abstract Missing data caused by boundary specification has a detrimental effect on the analysis of network structures, and designing optimal sampling methods is crucial for conducting network investigations. …”
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基于灰色预测理论的加速试验数据可靠性评估模型 by 蒋玉婷, 程世娟, 殷泽凯

“…At the case where the amount of failure data is small and the acceleration model is difficult to be determined,it is difficult for the traditional model to make a more accurate assessment of the testing results.Based on the grey prediction theory,the constant-experience data of life-obeying Weibull distribution was analyzed.The stress-related weights were used to generate background values to complement the missing data,and an equally spaced gray prediction model was established to correct the parameters of the accelerated life prediction model in this work.The analysis of the example indicated that the gray acceleration testing data evaluation model had a small relative error and high prediction accuracy.…”
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Research progress on electromagnetic spectrum multidimensional situation compressed mapping technology by Feng SHEN, Guoru DING, Jie LI, Bo ZHOU, Qihui WU

“…In the increasingly complex electromagnetic spectrum environment, accurately obtaining comprehensive spectrum situation is a crucial prerequisite for making precise spectrum decisions.First, the spectrum mapping was introduced and compared with spectrum sensing.Then, an in-depth review of existing spectrum situation generation methods was conducted.Next, multidimensional spectrum situation compressed mapping in the face of challenges such as heterogeneity, large scale missing data, time variability and environmental complexity was proposed.It effectively compensated for the incompleteness of the spectrum mapping framework caused by ignoring the spectrum situation sensing process in traditional spectrum situation generation methods.This could further provide more accurate guidance for enhancing spectrum utilization efficiency, strengthening spectrum security maintenance, and intensifying electromagnetic warfare decision-making.Lastly, the future development trends of spectrum compressed mapping were discussed.…”
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Optimal Imputation Methods under Stratified Ranked Set Sampling by Shashi Bhushan, Anoop Kumar

“…This paper is fundamental effort to suggest some combined and separate imputation methods in presence of missing data under SRSS. It has been shown that the proposed imputation methods become superior than the mean imputation method, ratio imputation method, Diana and Perri (2010) type imputation method and Sohail et al. (2018) type imputation methods. …”
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Grading Prediction of Enterprise Financial Crisis Based on Nonlinear Programming Evaluation: A Case Study of Chinese Transportation Industry by Zhi-yuan Li

“…The proposed model can deal with the case of missing data, and has the good isotonic property and profound theoretical background. …”
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A Missing Sensor Data Estimation Algorithm Based on Temporal and Spatial Correlation by Zhipeng Gao, Weijing Cheng, Xuesong Qiu, Luoming Meng

“…To address this problem, Temporal and Spatial Correlation Algorithm (TSCA) is proposed to estimate missing data as accurately as possible in this paper. …”
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